Realtime Recognition of Attention State by Complexity Analysis of Electroencephalogram

Li Yu Huang; Hong Juan Ma; Lang Jin; Rong Lu

doi:10.4028/www.scientific.net/AMM.385-386.1457

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 385-386Realtime Recognition of Attention State by...

Realtime Recognition of Attention State by Complexity Analysis of Electroencephalogram

Abstract:

The object of this paper is presenting a novel approach to classify the attention state and non-attention state. Firstly, the raw recorded electroencephalogram (EEG) data were decomposed by the algorithm of wavelet packet, several main EEG rhythms were extracted; then a complexity measure of these rhythm signal, approximate entropy (ApEn) was calculated respectively, and the values were used as input vector of a trained support vector machine (SVM), the output of this SVM will be the result of classification. The average performance obtained for the proposed scheme in classification is: sensitivity 73.7%, specificity 71.4% and accuracy 72.5%.

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Periodical:

Applied Mechanics and Materials (Volumes 385-386)

Pages:

1457-1460

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.385-386.1457

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Online since:

August 2013

Authors:

Li Yu Huang, Hong Juan Ma, Lang Jin, Rong Lu

Keywords:

Approximate Entropy, Electroencephalogram, Support Vector Machine (SVM), Wavelet Transform (WT)

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